linear_LDE: Local Discriminant Embedding

In Rdimtools: Dimension Reduction and Estimation Methods

Local Discriminant Embedding

Description

Local Discriminant Embedding (LDE) is a supervised algorithm that learns the embedding for the submanifold of each class. Its idea is to same-class data points maintain their original neighborhood information while segregating different-class data distinct from each other.

Usage

do.lde(
  X,
  label,
  ndim = 2,
  t = 1,
  numk = max(ceiling(nrow(X)/10), 2),
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations.
label	a length-n vector of data class labels.
ndim	an integer-valued target dimension.
t	kernel bandwidth in (0,∞).
numk	the number of neighboring points for k-nn graph construction.
preprocess	an additional option for preprocessing the data. Default is "center". See also aux.preprocess for more details.

Value

a named list containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

trfinfo

a list containing information for out-of-sample prediction.

projection

a (p\times ndim) whose columns are basis for projection.

Author(s)

Kisung You

References

\insertRef

hwann-tzongchen_local_2005Rdimtools

Examples

## generate data of 2 types with clear difference
set.seed(100)
diff = 15
dt1  = aux.gensamples(n=50)-diff;
dt2  = aux.gensamples(n=50)+diff;

## merge the data and create a label correspondingly
X      = rbind(dt1,dt2)
label  = rep(1:2, each=50)

## try different neighborhood size
out1 <- do.lde(X, label, numk=5)
out2 <- do.lde(X, label, numk=10)
out3 <- do.lde(X, label, numk=25)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="LDE::k=5")
plot(out2$Y, pch=19, col=label, main="LDE::k=10")
plot(out3$Y, pch=19, col=label, main="LDE::k=25")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.